CN112464493B - Improved Model, Regional Runoff and Flood Risk Design Method Based on TOPMODEL Model - Google Patents

Abstract

An improved method based on the TOPMODEL model, which involves the fields of water cycle simulation and hydrological design. Based on the current TOPMODEL model, it makes up for the current TOPMODEL model by constructing potential evapotranspiration calculations that integrate different data requirements, unit line surface confluence and other mechanisms. It relies heavily on pan observation data, and the confluence calculation is difficult to reflect the role of river network regulation and storage. Compared with the current TOPMODEL model, the improved model improves the applicability and accuracy of flow data inversion in data-deficient areas. A method for regional runoff and flood risk design, which is calculated using the above improved method. It can provide strong theoretical and technical support for design section runoff, flood design and analysis decision-making in data-deficient areas.

Description

Improved model based on TOPMODEL model, design of regional runoff and flood risk method

technical field

The invention relates to the fields of water cycle simulation and hydrological design, in particular to an improved model based on the TOPMODEL model and a method for regional runoff and flood risk design.

Background technique

Compared with other watershed hydrological models, the TOPMODEL model has remarkable features because it combines the characteristics of conceptual hydrological models and distributed hydrological models: ① It is based on a certain physical mechanism, but its structure is relatively refined and not too complicated; ② It requires less data types , the input data is easier to prepare; ③ there are not many model parameters, and the parameter selection is convenient; ④ the application is very wide, and the effect is generally tested well. Because of the above characteristics, the TOPMODEL model is widely used in related research and business such as watershed hydrological cycle simulation.

Although the inversion of flow data in data-deficient areas belongs to the category of watershed hydrological cycle simulation research, according to the model theory and structural analysis, the current TOPMODEL model applied to flow data inversion in data-deficient areas has the following three defects: ①In data-deficient areas, often There is a lack of or even no pan observation data, and the current model relies heavily on pan observation data due to the lack of potential evapotranspiration calculation mechanism, which makes the model difficult to apply. ② There is a lack of calculation mechanism for snowmelt runoff, and the accuracy of inversion of flow in alpine and high-altitude watersheds is not high. ③The current confluence algorithm of the model uses the isocurrent timeline method, which cannot fully reflect the regulation and storage function of the watershed, and cannot perform confluence calculations separately for surface and groundwater sources, and the confluence calculation accuracy is not high. The above deficiencies lead to poor or even difficult application of the TOPMODEL model in flow data inversion research in data-deficient areas.

From the perspective of vertical application extension, the most direct application of flow data inversion in data-deficient areas is to design cross-section runoff and flood design. With the development of professional technologies such as computer, remote sensing, and hydrology, more and more watershed hydrological models are used in this area, but the relevant applications are based on the idea of deterministic design, and the deterministic design results of runoff and flood are obtained, which cannot be fully realized. Due to the risk of uncertainty in data, models, parameters and other factors in the evaluation results, it brings difficulties in decision-making for engineering design.

Contents of the invention

The purpose of the present invention is to provide an improved model based on the TOPMODEL model, which has a scientific and reasonable design, optimizes the traditional TOPMODEL model, and improves the applicability and accuracy of flow data inversion in data-deficient areas.

Another object of the present invention is to provide a method for regional runoff and flood risk design, which, based on the above-mentioned improved model, can provide strong theoretical and technical support for cross-sectional runoff design, flood design and analysis decision-making in areas lacking data.

Embodiments of the present invention are achieved like this:

A kind of improved model based on TOPMODEL model, it comprises:

According to the collected environmental parameters, match the corresponding potential evapotranspiration calculation formula, calculate the potential evapotranspiration, and apply it to the TOPMODEL model.

A method for regional runoff and flood risk design, which is calculated using the above-mentioned improved model.

The beneficial effects of the embodiments of the present invention are:

The embodiment of the present invention provides an improved model based on the TOPMODEL model. On the basis of the current TOPMODEL model, it makes up for the problem that the current TOPMODEL model relies heavily on evaporating pan observation data by constructing a potential evapotranspiration calculation mechanism that integrates different data requirements. , compared with the current TOPMODEL model, the improved model improves the applicability and accuracy of flow data inversion in data-deficient areas.

An embodiment of the present invention also provides a method for regional runoff and flood risk design, which uses the above-mentioned improved model for calculation. It can provide strong theoretical and technical support for design section runoff, flood design and analysis decision-making in data-deficient areas.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and thus It should be regarded as a limitation on the scope, and those skilled in the art can also obtain other related drawings based on these drawings without creative work.

Fig. 1 is the schematic diagram of the improved model based on TOPMODEL model provided by the embodiment of the present invention and the method for regional runoff and flood risk design;

Fig. 2 is the annual runoff frequency analysis (assessment and approval) of Mengwei Hydrological Station provided by the embodiment of the present invention;

Fig. 3 is the annual runoff frequency analysis of the Mengwei Hydrological Station provided by the embodiment of the present invention (optimum parameter calculation flow);

Fig. 4 is the annual runoff frequency analysis (33.3% lower boundary flow) of Mengwei Hydrological Station provided by the embodiment of the present invention;

Fig. 5 is the annual runoff frequency analysis (weighted flow) of Mengwei Hydrological Station provided by the embodiment of the present invention;

Fig. 6 is the annual flood peak frequency analysis (assessment and approval) of Mengwei Hydrological Station provided by the embodiment of the present invention;

Fig. 7 is the annual flood peak frequency analysis (optimum parameter calculation flow) of Mengwei hydrological station provided by the embodiment of the present invention;

Fig. 8 is the annual flood peak frequency analysis (upper boundary discharge) of Mengwei hydrological station provided by the embodiment of the present invention;

Fig. 9 is the annual flood peak frequency analysis (weighted flow) of the Mengwei hydrological station provided by the embodiment of the present invention.

Detailed ways

An improved model based on the TOPMODEL model and a method for regional runoff and flood risk design in an embodiment of the present invention will be described in detail below.

A kind of improved model based on TOPMODEL model, it comprises:

According to the collected environmental parameters, match the corresponding potential evapotranspiration calculation formula, calculate the potential evapotranspiration, and apply it to the TOPMODEL model.

In the existing technology, pan observation data are often lacking or even non-existent in data-deficient areas, and the existing TOPMODEL model relies heavily on pan observation data due to the lack of potential evapotranspiration calculation mechanism, which makes the model difficult to apply. In order to solve the above problems, the embodiment of the present invention selects existing formulas for calculating evapotranspiration and compiles them into a library. Then, according to the available environmental parameters, match the appropriate calculation formula, and solve the application problem of the TOPMODEL model in the data-deficient areas.

Further, when the available environmental parameters include daily average temperature, daily maximum temperature and daily minimum temperature, the potential evapotranspiration is calculated using Hargreaves (1985) formula.

If A<0.75, the formula for calculating potential evapotranspiration is

If A≥0.75, the formula for calculating potential evapotranspiration is

In the formula, ET is the potential evapotranspiration, A=Krs*0.0135*(T _max -T _min ) ^0.5 , Krs is 0.16-0.19, when the measurement area is located inland, Krs can take a value of 0.16, and when it is located in the coastal area, The possible value is 0.19. T _max is the daily maximum temperature, T _min is the daily minimum temperature, and T _a is the daily average temperature.

Furthermore, when the available environmental parameters include the daily average sunshine hours in addition to the daily average temperature, the formula for calculating potential evapotranspiration is replaced by the Hargreaves (1975) formula,

In the formula, λ is the latent heat of evaporation, which can be calculated according to the daily average temperature; R _s is the short-wave radiation, which can be calculated according to the daily average sunshine hours, point location elevation and latitude; other parameters are the same as Hargreaves (1985) formula.

Further, when the available environmental parameters include daily average relative humidity in addition to daily average temperature and daily sunshine hours, the formula for calculating potential evapotranspiration is replaced by the Turc formula.

If RH≥50, the formula for calculating potential evapotranspiration is

If RH<50, the formula for calculating potential evapotranspiration is

In the formula, RH is the daily average relative humidity, and other parameters are the same as Hargreaves (1975) formula.

Furthermore, in addition to the daily average temperature, daily average relative humidity, and daily average sunshine hours (or daily average daily short-wave radiation into the ground), the available environmental parameters also increase the daily average wind speed, and the potential evapotranspiration The calculation formula is replaced by the Penman-Monteith formula,

In the formula, Δ is the corresponding slope of the saturated water pressure-temperature curve, which can be calculated according to the daily average temperature; G is the soil heat flux density, which generally can be taken as 0; γ is the hygrometer constant, which can be calculated according to the elevation of the point position U ₂ is the wind speed at a height of 2m, which can be calculated according to the point elevation and the daily average wind speed; _es is the saturated water vapor pressure, which can be calculated indirectly according to the daily average temperature; e _a is the actual water vapor pressure, which can be indirectly calculated according to The temperature and daily average relative humidity are calculated; R _n is the net radiation, which can be calculated according to the daily average sunshine hours (or the daily average daily short-wave radiation into the ground), the elevation and latitude of the point location, and the daily average temperature.

The selection of the above formulas adopts a progressive form, and each time some environmental parameters are added, a more suitable formula with better calculation accuracy will be automatically matched, thereby improving the accuracy of the results.

In addition, a kind of improved model based on TOPMODEL model provided by the embodiment of the present invention also includes:

The snowmelt runoff calculation formula is introduced to simulate the snow accumulation and snowmelt process, and the original rainfall input is processed to obtain the net rain process; the calculation results are introduced into the TOPMODEL model; among them, the snowmelt runoff calculation formula is,

M _s =C _s (T _a -T _t ), (7)

In the formula, M _s represents the amount of snowmelt when it is positive, and snow accumulation when it is negative, C _s is the degree-day factor, T _t is the critical temperature, and T _a is the daily average temperature.

If the period rainfall is P and the previous snow depth is S, then the snow depth at the end of the calculation period is SM _s (not lower than 0), and the period net rain is P+M _s (M _s <S) or P+S (M _s ≥ S).

The existing TOPMODEL model lacks a snowmelt runoff calculation mechanism, and the accuracy of inversion of the flow in alpine and high-altitude watersheds is not high. However, the embodiment of the present invention is more suitable for the calculation of snowmelt runoff in areas with high coldness and high altitude and lack of data, and further improves the calculation accuracy under the condition that only one degree-day factor parameter is added.

Further, a kind of improved model based on TOPMODEL model provided by the embodiment of the present invention also includes:

The surface confluence algorithm of the Nash landform unit line and the linear reservoir underground confluence algorithm are used to replace the isocurrent timeline confluence algorithm of the TOPMODEL model.

① For the surface confluence part, the surface confluence algorithm of the Nash geomorphic unit line is adopted. The basic form of the Nash geomorphic unit line is as follows:

In the formula: n is a parameter reflecting the regulation and storage capacity of the watershed, which is equivalent to the number of linear reservoirs or the number of reservoir adjustments, K is the storage and discharge coefficient of a linear reservoir, which has a time dimension; Γ(n) is a Γ function, namely

According to the surface yield in each calculation period, the surface runoff in a certain period of time in the future can be calculated through the above convolution equation, and the final surface runoff process at the outlet of the watershed can be obtained by superimposing the surface runoff processes in each period.

In derivation of parameter n and parameter K, it can be deduced according to Horton landform geometric ratio (area ratio, river length ratio, bifurcation ratio):

In the formula: R _B , _RL , _RA — the bifurcation ratio, river length ratio and area ratio of the watershed system, which can be obtained from DEM data based on the Straller level.

The essence of deriving the K parameter is how to determine the average confluence time of the watershed based on topographic data. According to the fact that the flow velocity of rivers of different grades mainly depends on the slope of their topography, the following relationship is expressed:

τ=1-(1-λ)(1-ρ) (10)

in:

Further analysis can also get the following relationship:

τ=λ ^1-mλ (12)

It can also be deduced from formula (10) and formula (11):

Using Horton's law, it can be deduced that:

In the above formula: τ is the ratio of the average confluence time of the net rain particles from the river source to a section downstream to the average confluence time of the river source to the outlet section of the basin; ρ is a parameter related to the length of the river and the gradient of the river bottom; The number of sub-river sections from the river source to a certain section downstream; N is the number of sub-river sections from the river source to the outlet section of the basin; Δl _j is the length of the jth sub-river section divided from the river source; p _j is the average slope of the j-th sub-river section; m is a comprehensive parameter reflecting the characteristics of the longitudinal section of the river channel; Ω is the series of the highest level river in the river system; V _Ω is the flow velocity of the outlet section of the watershed, which is generally given by the average flow velocity of the flood section of the flood hydrograph of the outlet section; α is The ratio of the distance from the centroid of the watershed to the outlet section of the watershed to the length of the watershed.

The present invention has some innovations in deriving the m parameter. The m parameter can be considered as a comprehensive parameter reflecting the characteristics of the longitudinal section of the channel. At present, it is necessary to combine the actual data of the main and tributary streams, first calculate τ and λ, and draw the τ～λ diagram for analysis. , is more complicated.

According to Horton's river length law and slope law, the present invention proposes the concept of river length slope ratio R _LS for calculating the ρ parameter, which is the average ratio of the lp ^-0.5 values of rivers at all levels, then:

On this basis, formula (10) and formula (12) can be combined, and the parameter m can be calculated more conveniently through iterative solution.

② The underground confluence part adopts the linear reservoir algorithm, and the basic form of the linear reservoir is as follows:

Since the water surface gradient of groundwater is relatively gentle, it can be considered that the relationship between its fluctuation and storage and discharge is the same, and the linear reservoir calculation formula is:

Q _g2 =R _g (1-CG)U+Q _g1 CG (16)

In the formula: R _g is the base flow depth; CG is the groundwater receding coefficient; Q _g1 and Q _g2 are the base flow in the previous period and the current period respectively; U is the unit conversion coefficient.

In each calculation period, the underground runoff is iteratively calculated according to the surface runoff and the underground runoff using formula (16).

By combining the surface confluence algorithm of Nash landform unit line and the linear reservoir underground confluence algorithm, the isocurrent timeline algorithm with defects in theory and structure is replaced.

A method for regional runoff and flood risk design, which is calculated using the above-mentioned improved model.

Preferably, the method uses the GLUE algorithm and the improved model for coupled calculation.

Further, it includes:

The Monte Carlo algorithm is used to randomly generate multiple parameter groups, which are respectively substituted into the improved model to calculate the simulated flow process, and the corresponding likelihood function values are calculated in combination with the actual flow process, and then the weights of each parameter group are calculated, combined with the set confidence index , according to the weight accumulation and flow sorting, the uncertainty upper boundary flow process, lower boundary flow process, weighted flow process, and optimal parameter flow process of the corresponding confidence degree are calculated;

Based on the lower boundary flow process, weighted flow process, and theoretical optimal flow process, the long series of annual and monthly runoffs corresponding to different flow processes are calculated; based on the upper boundary flow process, weighted flow process, and optimal parameter flow process, different flow rates are calculated. The long-series annual and monthly flood peaks corresponding to the process; the long-series annual and monthly runoff and the long-series annual and monthly flood peak results are substituted into the frequency calculation method to obtain the corresponding runoff and flood calculation results of different series.

Furthermore, the coupling GLUE universal likelihood uncertainty analysis algorithm, the specific algorithm has been described in relevant literature, and this description will not go into details. Based on the basic principles of the GLUE algorithm, the present invention has certain characteristic settings in order to speed up the calculation efficiency, which are introduced as follows:

The Monte Carlo random sampling method is used to randomly generate tens of thousands of parameter groups, and the parameters of the improved model are evaluated. On the basis of a certain likelihood function threshold setting, multiple effective parameter groups are screened. The weights of each effective parameter group are:

In the formula: Weight _i is the weight of the i-th effective parameter group; L _i is the likelihood value of the i-th effective parameter group; n is the total number of effective parameter groups.

On this basis, for each calculation period, use the bubble algorithm to sort the flow values, according to a certain degree of confidence Z, based on the cumulative calculation of the weight value, take the selection of the upper boundary of the flow as an example:

If accumulated:

In the formula: a is the cumulative serial number in a certain order.

Then Q _a is the upper boundary flow of the corresponding calculation period.

Similarly, the selection of the flow lower boundary is based on a principle similar to that of selecting the flow upper boundary, except that it is just opposite in the direction of weight value accumulation.

The coupling GLUE universal likelihood uncertainty analysis algorithm proposed by the present invention, compared with the deterministic method currently used, can obtain the upper and lower uncertain bounds of the flow process under a certain degree of confidence, combined with frequency analysis, can obtain the corresponding confidence of runoff and flood Radical or conservative design results under different degrees can provide strong theoretical and technical support for cross-sectional runoff design and flood design and analysis decisions in data-deficient areas.

The characteristics and performance of the present invention will be described in further detail below in conjunction with the examples.

Example

Step 1: Study and select the daily average temperature, daily average actual water vapor pressure, daily average wind speed and daily average daily short-wave radiation in the control basin of Mengwei Station from 1994 to 2002 retrieved by the climate model, and substitute it into the potential of integrating different data requirements. The evapotranspiration calculation mechanism calculates the potential evapotranspiration of the watershed, which is used as the input of evapotranspiration in data-deficient areas.

Step 2: Based on the current (unimproved) TOPMODEL model, the runoff calculation mechanism remains unchanged, and the original isocurrent timeline mechanism is replaced by the calculation of surface runoff using the Nash landform unit line surface flow algorithm, and the use of linear reservoir underground flow The improved mechanism of algorithm calculation of subsurface runoff is added with the calculation mechanism of snowmelt runoff based on degree-day factor. After the model is running, the snowmelt yield is calculated time by time, and the precipitation input is corrected. Through the above improvements, an improved model is obtained.

Step 3: Set the parameter range. Different from the empirical setting of the parameter range of the current TOPMODEL model, the setting of the parameter range of the improved model is also more scientific and reasonable: the parameter n is obtained based on the Straler level and the DEM data according to formula (2); The value range of the parameter K can be further determined according to the flow velocity range specified by the cross-section survey data; the linear reservoir parameters can be obtained by directly analyzing the measured flow data of the receding section without precipitation; the degree-day factor parameter can be obtained by The range is empirically drawn up for the type of climatic region; the method for determining the range of other parameters is consistent with the current TOPMODEL model.

Step 4: Coupling the GLUE algorithm with the improved model. Based on the result of the parameter range in Step 3, multiple (generally more than 5,000 times) parameter groups are randomly generated using the Monte Carlo algorithm, respectively substituted into the improved model, and the simulated flow process is calculated. Combined with the actual flow process, the corresponding likelihood function value (similar to However, the function is optional, and the default is the Nash efficiency coefficient), and then calculate the weight of each parameter group, combined with the artificially set confidence index, according to the weight accumulation and flow sorting, calculate the uncertainty upper boundary flow process of the corresponding confidence degree, The lower boundary flow process, the weighted flow process (obtained by weighting according to the weight of the parameter group), and the optimal parameter flow process (corresponding to the calculation result of the parameter group with the largest likelihood function value). During the implementation of this step, the GLUE algorithm repeatedly calls the improved model.

Step 5: Based on the lower boundary flow process obtained in step 4 (the confidence level is generally 33.3%), the weighted flow process, and the theoretical optimal flow process, calculate the long series of annual and monthly runoff corresponding to different flow processes; The upper boundary flow process (the confidence level is generally 90%), the weighted flow process, and the optimal parameter flow process are calculated to obtain the long series of annual and monthly flood peaks corresponding to different flow processes. The annual and monthly runoff of each long series and the annual and monthly flood peak results of the long series are substituted into the frequency calculation method to obtain the corresponding runoff and flood calculation results of different series.

See Figures 2 to 9 and Tables 1 to 2 for the results after the implementation of the technical solution. From which it can be seen that:

① In terms of runoff design, the calculation results of annual runoff frequency obtained from the optimal parameter flow process and weighted flow process are generally close to the results of the review and verification, while the calculation results of annual runoff frequency obtained from the boundary flow results under the 33.3% confidence level are compared with the results of the review and verification The results are generally small except that the dry season is slightly larger. It can be seen that the annual runoff frequency calculation results obtained from the boundary flow results under the 33.3% confidence level can be used as the control results for the small runoff. The comprehensive analysis shows that the method of the invention can obtain runoff average and conservative risk design results, and can better serve decision makers compared with deterministic runoff design results.

②In terms of flood design, the calculation results of the annual flood peak frequency obtained by the optimal parameter flow process are generally close to the review and verification results, while the calculation results of the annual flood peak frequency obtained by the boundary flow at the 90% confidence level are generally larger than the review and verification results. However, the calculation results of the annual flood peak frequency obtained by weighted flow are generally smaller than the results of the review and approval. Comprehensive analysis shows that the risk design results of the conservative and partial (radical) floods that can be obtained through the present invention, combined with the frequency calculation results obtained by calculating the flow of optimal parameters, provide a variety of options, compared with deterministic flood design As a result, decision makers can make risk decisions based on specific considerations.

Table 1 The annual runoff frequency calculation result table of Mengwei Station calculated by the present invention

method average Cv Cs/Cv P = 10% P=50% P=90% Review and approval 436 0.26 2 586 426 299 Optimal parameter calculation flow 458 0.20 2 579 452 345 weighted flow 485 0.19 2 606 479 371 33% confidence lower bound 414 0.21 2 529 408 307

Table 2 The annual flood peak frequency calculation result table of Mengwei Station calculated by the present invention

method average Cv Cs/Cv P=0.01% P=0.02% P=0.05% P=0.1% P=1% P=5% Review and approval (benchmark) 3690 0.56 3.5 20500 19100 17200 15800 11000 7810 Optimal parameter calculation flow 3550 0.59 3.5 21100 19600 17600 16100 11200 7750 upper boundary flow 6500 0.36 5 24700 23200 21200 19700 14700 11100 weighted flow 3810 0.5 3 17300 16300 14900 13800 10200 7530

To sum up, the embodiment of the present invention provides an improved model based on the TOPMODEL model. On the basis of the current TOPMODEL model, it makes up for the current TOPMODEL model that relies heavily on evaporation by constructing a potential evapotranspiration calculation mechanism that integrates different data requirements. Compared with the current TOPMODEL model, the improved model improves the applicability and accuracy of flow data inversion in data-deficient areas.

An embodiment of the present invention also provides a method for regional runoff and flood risk design, which uses the above-mentioned improved model for calculation. It can provide strong theoretical and technical support for design section runoff, flood design and analysis decision-making in data-deficient areas.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims (3)

1. a kind of improved method based on TOPMODEL model, it is characterized in that, comprising: According to the collected environmental parameters, match the corresponding potential evapotranspiration calculation formula, calculate the potential evapotranspiration, and apply it to the TOPMODEL model; The environmental parameters include daily average temperature, daily maximum temperature and daily minimum temperature, and the formula for calculating potential evapotranspiration is:

( A <0.75), or

( A≥0.75 ); In the formula, ET is the potential evapotranspiration,

, Krs is 0.16~0.19, T _max is the daily maximum temperature, T _min is the daily minimum temperature, T _a is the daily average temperature,

is the latent heat of evaporation; If the environmental parameters include daily average temperature and daily average sunshine hours, the formula for calculating potential evapotranspiration is replaced by,

; In the formula,

is the latent heat of evaporation; R _s is the amount of short-wave radiation; If the environmental parameters include daily average temperature, daily average relative humidity and daily average sunshine hours, the formula for calculating potential evapotranspiration is replaced by,

( RH ≥ 50), or

( RH <50); In the formula, RH is the daily average relative humidity; If the environmental parameters include daily average temperature, daily average relative humidity, daily average wind speed and daily average sunshine hours, the formula for calculating potential evapotranspiration is replaced by,

; In the formula,

is the corresponding slope of the saturated water pressure-temperature curve; G is the soil heat flux density;

is the hygrometer constant; U ₂ is the wind speed at a height of 2m; es _is the saturated water vapor pressure; e _a is the actual water vapor pressure; R _n is the net radiation; The improved method also includes: Introduce the calculation formula of snowmelt runoff to simulate snow accumulation and snowmelt process, process the original rainfall input, and obtain the net rain process; introduce the calculation result into the TOPMODEL model; wherein, the calculation formula of snowmelt runoff is,

, In the formula, M _s is the amount of snowmelt or snow accumulation, C _s is the degree-day factor, T _t is the critical temperature, T _a is the daily average temperature; The improved method also includes: The surface confluence algorithm of the Nash geomorphic unit line and the linear reservoir underground confluence algorithm are used to replace the isocurrent timeline confluence algorithm of the TOPMODEL model. 2. A method for regional runoff and flood risk design, characterized in that, adopt the improved method as claimed in claim 1 to carry out coupled calculation with GLUE algorithm. 3. The method for regional runoff and flood risk design according to claim 2, characterized in that, comprising: The Monte Carlo algorithm is used to randomly generate multiple parameter groups, which are respectively substituted into the improved method to calculate the simulated flow process, and the corresponding likelihood function values are obtained by combining the calculation of the actual flow process, and then calculate the weight of each parameter group, combined with the settings The confidence index of , according to the weight accumulation and flow sorting, calculate the uncertainty upper boundary flow process, lower boundary flow process, weighted flow process, and optimal parameter flow process of the corresponding confidence degree; Based on the lower boundary flow process, the weighted flow process, and the optimal parameter flow process, the long series of annual and monthly runoffs corresponding to different flow processes are calculated; based on the upper boundary flow process, the weighted flow process, The optimal parameter flow process is calculated to obtain the corresponding long-series annual and monthly flood peaks of different flow processes; the long-series annual and monthly runoff and the long-series annual and monthly flood peak results are substituted into the frequency calculation method to obtain the corresponding runoff and monthly flood peaks of different series. Flood calculation results.

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